{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "bb1af397-500c-4a88-8168-abc57a1aa513",
   "metadata": {},
   "source": [
    "# End-to-end Learning with Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d79b1ed0-1dcc-4a62-a9dd-09c13eec0b80",
   "metadata": {},
   "source": [
    "In this notebook, you will learn how to implement an end-to-end communication system as an autoencoder [1].\n",
    "The implemented system is shown in the figure below.\n",
    "An additive white Gaussian noise (AWGN) channel is considered.\n",
    "On the transmitter side, joint training of the constellation geometry and bit-labeling is performed, as in [2].\n",
    "On the receiver side, a neural network-based demapper that computes log-likelihood ratios (LLRs) on the transmitted bits from the received samples is optimized.\n",
    "The considered autoencoder is benchmarked against a quadrature amplitude modulation (QAM) with Gray labeling and the optimal AWGN demapper."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4694269-5aee-4623-9ab0-6a9f84d9956c",
   "metadata": {},
   "source": [
    "![System Model]()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2920adb0-9dd0-484d-9496-cfbf87af5eaa",
   "metadata": {},
   "source": [
    "Two algorithms for training the autoencoder are implemented in this notebook:\n",
    "\n",
    "* Conventional stochastic gradient descent (SGD) with backpropagation, which assumes a differentiable channel model and therefore optimizes the end-to-end system by backpropagating the gradients through the channel (see, e.g., [1]).\n",
    "* The training algorithm from [3], which does not assume a differentiable channel model, and which trains the end-to-end system by alternating between conventional training of the receiver and reinforcement learning (RL)-based training of the transmitter. Compared to [3], an additional step of fine-tuning of the receiver is performed after alternating training.\n",
    "\n",
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3287acf2-0dcd-419f-8cfc-27ea29ca3af6",
   "metadata": {},
   "source": [
    "* [GPU Configuration and Imports](#GPU-Configuration-and-Imports)\n",
    "* [Simulation Parameters](#Simulation-Parameters)\n",
    "* [Neural Demapper](#Neural-Demapper)\n",
    "* [Trainable End-to-end System: Conventional Training](#Trainable-End-to-end-System:-Conventional-Training)\n",
    "* [Trainable End-to-end System: RL-based Training](#Trainable-End-to-end-System:-RL-based-Training)\n",
    "* [Evaluation](#Evaluation)\n",
    "* [Visualizing the Learned Constellations](#Visualizing-the-Learned-Constellations)\n",
    "* [References](#References)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d35352-0937-4710-9f2c-7e50f819ea2c",
   "metadata": {},
   "source": [
    "## GPU Configuration and Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e201e1d-e0d8-4133-b251-14823c25f68b",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-09T00:53:33.030795Z"
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   "outputs": [],
   "source": [
    "import os\n",
    "if os.getenv(\"CUDA_VISIBLE_DEVICES\") is None:\n",
    "    gpu_num = 0 # Use \"\" to use the CPU\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = f\"{gpu_num}\"\n",
    "os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n",
    "\n",
    "# Import Sionna\n",
    "try:\n",
    "    import sionna.phy\n",
    "except ImportError as e:\n",
    "    import sys\n",
    "    if 'google.colab' in sys.modules:\n",
    "       # Install Sionna in Google Colab\n",
    "       print(\"Installing Sionna and restarting the runtime. Please run the cell again.\")\n",
    "       os.system(\"pip install sionna\")\n",
    "       os.kill(os.getpid(), 5)\n",
    "    else:\n",
    "       raise e\n",
    "\n",
    "# Configure the notebook to use only a single GPU and allocate only as much memory as needed\n",
    "# For more details, see https://www.tensorflow.org/guide/gpu\n",
    "import tensorflow as tf\n",
    "gpus = tf.config.list_physical_devices('GPU')\n",
    "if gpus:\n",
    "    try:\n",
    "        tf.config.experimental.set_memory_growth(gpus[0], True)\n",
    "    except RuntimeError as e:\n",
    "        print(e)\n",
    "# Avoid warnings from TensorFlow\n",
    "tf.get_logger().setLevel('ERROR')\n",
    "\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Layer, Dense\n",
    "\n",
    "from sionna.phy import Block\n",
    "from sionna.phy.channel import AWGN\n",
    "from sionna.phy.utils import ebnodb2no, log10, expand_to_rank\n",
    "from sionna.phy.fec.ldpc import LDPC5GEncoder, LDPC5GDecoder\n",
    "from sionna.phy.mapping import Mapper, Demapper, Constellation, BinarySource\n",
    "from sionna.phy.utils import sim_ber\n",
    "\n",
    "sionna.phy.config.seed = 42 # Set seed for reproducible random number generation\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fca65b28-74a4-4cd9-8c66-748259d05e57",
   "metadata": {},
   "source": [
    "## Simulation Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ddfd420b-0f45-468a-b292-8aaea2ce280f",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "###############################################\n",
    "# SNR range for evaluation and training [dB]\n",
    "###############################################\n",
    "ebno_db_min = 4.0\n",
    "ebno_db_max = 8.0\n",
    "\n",
    "###############################################\n",
    "# Modulation and coding configuration\n",
    "###############################################\n",
    "num_bits_per_symbol = 6 # Baseline is 64-QAM\n",
    "modulation_order = 2**num_bits_per_symbol\n",
    "coderate = 0.5 # Coderate for the outer code\n",
    "n = 1500 # Codeword length [bit]. Must be a multiple of num_bits_per_symbol\n",
    "num_symbols_per_codeword = n//num_bits_per_symbol # Number of modulated baseband symbols per codeword\n",
    "k = int(n*coderate) # Number of information bits per codeword\n",
    "\n",
    "###############################################\n",
    "# Training configuration\n",
    "###############################################\n",
    "num_training_iterations_conventional = 10000 # Number of training iterations for conventional training\n",
    "# Number of training iterations with RL-based training for the alternating training phase and fine-tuning of the receiver phase\n",
    "num_training_iterations_rl_alt = 7000\n",
    "num_training_iterations_rl_finetuning = 3000\n",
    "training_batch_size = tf.constant(128, tf.int32) # Training batch size\n",
    "rl_perturbation_var = 0.01 # Variance of the perturbation used for RL-based training of the transmitter\n",
    "model_weights_path_conventional_training = \"awgn_autoencoder_weights_conventional_training\" # Filename to save the autoencoder weights once conventional training is done\n",
    "model_weights_path_rl_training = \"awgn_autoencoder_weights_rl_training\" # Filename to save the autoencoder weights once RL-based training is done\n",
    "\n",
    "###############################################\n",
    "# Evaluation configuration\n",
    "###############################################\n",
    "results_filename = \"awgn_autoencoder_results\" # Location to save the results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d711f03c-93b9-4524-a7a6-63a35b825623",
   "metadata": {},
   "source": [
    "## Neural Demapper"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caff5b7e-0ae4-4890-a45b-378afc5bf9a5",
   "metadata": {},
   "source": [
    "The neural network-based demapper shown in the figure above is made of three dense layers with ReLU activation.\n",
    "\n",
    "The input of the demapper consists of a received sample $y \\in \\mathbb{C}$ and the noise power spectral density $N_0$ in log-10 scale to handle different orders of magnitude for the SNR.\n",
    "\n",
    "As the neural network can only process real-valued inputs, these values are fed as a 3-dimensional vector\n",
    "\n",
    "$$\\left[ \\mathcal{R}(y), \\mathcal{I}(y), \\log_{10}(N_0) \\right]$$\n",
    "\n",
    "where $\\mathcal{R}(y)$ and $\\mathcal{I}(y)$ refer to the real and imaginary component of $y$, respectively.\n",
    "\n",
    "The output of the neural network-based demapper consists of LLRs on the `num_bits_per_symbol` bits mapped to a constellation point. Therefore, the last layer consists of ``num_bits_per_symbol`` units.\n",
    "\n",
    "**Note**: The neural network-based demapper processes the received samples $y$ forming a block individually. The [neural receiver notebook](https://nvlabs.github.io/sionna/phy/tutorials/Neural_Receiver.html) provides an example of a more advanced neural network-based receiver that jointly processes a resource grid of received symbols."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e636efd-9a46-4fd1-8790-b4cb1b63f31e",
   "metadata": {
    "execution": {
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   "source": [
    "class NeuralDemapper(Layer):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self._dense_1 = Dense(128, 'relu')\n",
    "        self._dense_2 = Dense(128, 'relu')\n",
    "        self._dense_3 = Dense(num_bits_per_symbol, None) # The feature correspond to the LLRs for every bits carried by a symbol\n",
    "\n",
    "    def call(self, y, no):\n",
    "\n",
    "        # Using log10 scale helps with the performance\n",
    "        no_db = log10(no)\n",
    "\n",
    "        # Stacking the real and imaginary components of the complex received samples\n",
    "        # and the noise variance\n",
    "        no_db = tf.tile(no_db, [1, num_symbols_per_codeword]) # [batch size, num_symbols_per_codeword]\n",
    "        z = tf.stack([tf.math.real(y),\n",
    "                      tf.math.imag(y),\n",
    "                      no_db], axis=2) # [batch size, num_symbols_per_codeword, 3]\n",
    "        llr = self._dense_1(z)\n",
    "        llr = self._dense_2(llr)\n",
    "        llr = self._dense_3(llr) # [batch size, num_symbols_per_codeword, num_bits_per_symbol]\n",
    "\n",
    "        return llr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d232019-1c8b-4d51-b125-a1f0a4189dac",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: Conventional Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4cdeeb8-879c-4ae1-a734-6a04ffa48dd3",
   "metadata": {},
   "source": [
    "The following cell defines an end-to-end communication system that transmits bits modulated using a trainable constellation over an AWGN channel.\n",
    "\n",
    "The receiver uses the previously defined neural network-based demapper to compute LLRs on the transmitted (coded) bits.\n",
    "\n",
    "As in [1], the constellation and neural network-based demapper are jointly trained through SGD and backpropagation using the binary cross entropy (BCE) as loss function.\n",
    "\n",
    "Training on the BCE is known to be equivalent to maximizing an achievable information rate [2].\n",
    "\n",
    "The following model can be instantiated either for training (`training = True`) or evaluation (`training = False`).\n",
    "\n",
    "In the former case, the BCE is returned and no outer code is used to reduce computational complexity and as it does not impact the training of the constellation or demapper.\n",
    "\n",
    "When setting `training` to `False`, an LDPC outer code from 5G NR is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2709aae3-7d40-4f35-a56f-79bd09a81e56",
   "metadata": {
    "execution": {
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   "source": [
    "class E2ESystemConventionalTraining(Model):\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "\n",
    "        self._training = training\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            # num_bits_per_symbol is required for the interleaver\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "\n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with qam points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=qam_points,\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "        # To make the constellation trainable, we need to create seperate\n",
    "        # variables for the real and imaginary parts\n",
    "        self.points_r = self.add_weight(shape=qam_points.shape,\n",
    "                                        initializer=\"zeros\")\n",
    "        self.points_i = self.add_weight(shape=qam_points.shape,\n",
    "                                        initializer=\"zeros\")\n",
    "        self.points_r.assign(tf.math.real(qam_points))\n",
    "        self.points_i.assign(tf.math.imag(qam_points))\n",
    "\n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "        #################\n",
    "        # Loss function\n",
    "        #################\n",
    "        if self._training:\n",
    "            self._bce = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "\n",
    "    def call(self, batch_size, ebno_db):\n",
    "        # Set the constellation points equal to a complex tensor constructed\n",
    "        # from two real-valued variables\n",
    "        points = tf.complex(self.points_r, self.points_i)\n",
    "        self.constellation.points = points\n",
    "\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created as it is what is expected by some layers\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c) # x [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no) # [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        llr = tf.reshape(llr, [batch_size, n])\n",
    "        # If training, outer decoding is not performed and the BCE is returned\n",
    "        if self._training:\n",
    "            loss = self._bce(c, llr)\n",
    "            return loss\n",
    "        else:\n",
    "            # Outer decoding\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b,b_hat # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f347823-c28d-4a00-8a02-f9b5e6d450f3",
   "metadata": {},
   "source": [
    "A simple training loop is defined in the next cell, which performs `num_training_iterations_conventional` training iterations of SGD. Training is done over a range of SNR, by randomly sampling a batch of SNR values at each iteration."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d95542ed-a6be-4c72-bae5-5154ded0340b",
   "metadata": {},
   "source": [
    "**Note:** For an introduction to the implementation of differentiable communication systems and their optimization through SGD and backpropagation with Sionna, please refer to [the Part 2 of the Sionna tutorial for Beginners](https://nvlabs.github.io/sionna/phy/tutorials/Sionna_tutorial_part2.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab429a86-f606-420c-8ad1-e0efc6874584",
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     "shell.execute_reply": "2025-03-09T00:53:33.073322Z"
    }
   },
   "outputs": [],
   "source": [
    "def conventional_training(model):\n",
    "    # Optimizer used to apply gradients\n",
    "    optimizer = tf.keras.optimizers.Adam()\n",
    "\n",
    "    @tf.function(jit_compile=True)\n",
    "    def train_step():\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = tf.random.uniform(shape=[training_batch_size], minval=ebno_db_min, maxval=ebno_db_max)\n",
    "        # Forward pass\n",
    "        with tf.GradientTape() as tape:\n",
    "            loss = model(training_batch_size,  ebno_db)\n",
    "        # Computing and applying gradients\n",
    "        weights = model.trainable_variables\n",
    "        grads = tape.gradient(loss, weights)\n",
    "        optimizer.apply_gradients(zip(grads, weights))\n",
    "        return loss\n",
    "\n",
    "    for i in range(num_training_iterations_conventional):\n",
    "        loss = train_step()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print('Iteration {}/{}  BCE: {:.4f}'.format(i, num_training_iterations_conventional, loss.numpy()), end='\\r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5959e24-1107-4271-be5c-944eacdc3033",
   "metadata": {},
   "source": [
    "The next cell defines a utility function for saving the weights using [pickle](https://docs.python.org/3/library/pickle.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f32c0d68-55f1-41e1-88da-28b96c3cac0c",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2025-03-09T00:53:33.080337Z"
    }
   },
   "outputs": [],
   "source": [
    "def save_weights(model, model_weights_path):\n",
    "    weights = model.get_weights()\n",
    "    with open(model_weights_path, 'wb') as f:\n",
    "        pickle.dump(weights, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d6ad9e4-7df4-4988-bbb4-dedd0ba50f47",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5971c6da-52bf-46ab-91f0-57baf560964d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:53:33.083749Z",
     "iopub.status.busy": "2025-03-09T00:53:33.083411Z",
     "iopub.status.idle": "2025-03-09T00:54:02.979655Z",
     "shell.execute_reply": "2025-03-09T00:54:02.978617Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 9900/10000  BCE: 0.2894\r"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "model = E2ESystemConventionalTraining(training=True)\n",
    "conventional_training(model)\n",
    "# Save weights\n",
    "save_weights(model, model_weights_path_conventional_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e884151-1047-4cb7-b1ee-5610924128a3",
   "metadata": {},
   "source": [
    "## Trainable End-to-end System: RL-based Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d960a61-c6f2-400e-8a6b-ea1c7c2d2db7",
   "metadata": {},
   "source": [
    "The following cell defines the same end-to-end system as before, but stop the gradients after the channel to simulate a non-differentiable channel.\n",
    "\n",
    "To jointly train the transmitter and receiver over a non-differentiable channel, we follow [3], which key idea is to alternate between:\n",
    "\n",
    "- Training of the receiver on the BCE using conventional backpropagation and SGD.\n",
    "- Training of the transmitter by applying (known) perturbations to the transmitter output to enable estimation of the gradient of the transmitter weights with respect to an approximation of the loss function.\n",
    "\n",
    "When `training` is set to `True`, both losses for training the receiver and the transmitter are returned."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "60f00146-1051-4406-a3cd-4a6e3305b515",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:54:02.984064Z",
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     "shell.execute_reply": "2025-03-09T00:54:02.996137Z"
    }
   },
   "outputs": [],
   "source": [
    "class E2ESystemRLTraining(Model):\n",
    "\n",
    "    def __init__(self, training):\n",
    "        super().__init__()\n",
    "\n",
    "        self._training = training\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "\n",
    "        # Trainable constellation\n",
    "        # We initialize a custom constellation with qam points\n",
    "        qam_points = Constellation(\"qam\", num_bits_per_symbol).points\n",
    "        self.constellation = Constellation(\"custom\",\n",
    "                                           num_bits_per_symbol,\n",
    "                                           points=qam_points,\n",
    "                                           normalize=True,\n",
    "                                           center=True)\n",
    "        # To make the constellation trainable, we need to create seperate\n",
    "        # variables for the real and imaginary parts\n",
    "        self.points_r = self.add_weight(shape=qam_points.shape,\n",
    "                                        initializer=\"zeros\")\n",
    "        self.points_i = self.add_weight(shape=qam_points.shape,\n",
    "                                        initializer=\"zeros\")\n",
    "        self.points_r.assign(tf.math.real(qam_points))\n",
    "        self.points_i.assign(tf.math.imag(qam_points))\n",
    "\n",
    "        self._mapper = Mapper(constellation=self.constellation)\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        # We use the previously defined neural network for demapping\n",
    "        self._demapper = NeuralDemapper()\n",
    "        # To reduce the computational complexity of training, the outer code is not used when training,\n",
    "        # as it is not required\n",
    "        if not self._training:\n",
    "            self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def call(self, batch_size, ebno_db, perturbation_variance=tf.constant(0.0, tf.float32)):\n",
    "\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created as it is what is expected by some layers\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        # Outer coding is only performed if not training\n",
    "        if self._training:\n",
    "            c = self._binary_source([batch_size, n])\n",
    "        else:\n",
    "            b = self._binary_source([batch_size, k])\n",
    "            c = self._encoder(b)\n",
    "        # Modulation\n",
    "        # Set the constellation points equal to a complex tensor constructed\n",
    "        # from two real-valued variables\n",
    "        points = tf.complex(self.points_r, self.points_i)\n",
    "        self.constellation.points = points\n",
    "        x = self._mapper(c) # x [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        # Adding perturbation\n",
    "        # If ``perturbation_variance`` is 0, then the added perturbation is null\n",
    "        epsilon_r = tf.random.normal(tf.shape(x))*tf.sqrt(0.5*perturbation_variance)\n",
    "        epsilon_i = tf.random.normal(tf.shape(x))*tf.sqrt(0.5*perturbation_variance)\n",
    "        epsilon = tf.complex(epsilon_r, epsilon_i) # [batch size, num_symbols_per_codeword]\n",
    "        x_p = x + epsilon # [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x_p, no) # [batch size, num_symbols_per_codeword]\n",
    "        y = tf.stop_gradient(y) # Stop gradient here\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "\n",
    "        # If training, outer decoding is not performed\n",
    "        if self._training:\n",
    "            # Average BCE for each baseband symbol and each batch example\n",
    "            c = tf.reshape(c, [-1, num_symbols_per_codeword, num_bits_per_symbol])\n",
    "            bce = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(c, llr), axis=2) # Avergare over the bits mapped to a same baseband symbol\n",
    "            # The RX loss is the usual average BCE\n",
    "            rx_loss = tf.reduce_mean(bce)\n",
    "            # From the TX side, the BCE is seen as a feedback from the RX through which backpropagation is not possible\n",
    "            bce = tf.stop_gradient(bce) # [batch size, num_symbols_per_codeword]\n",
    "            x_p = tf.stop_gradient(x_p)\n",
    "            p = x_p-x # [batch size, num_symbols_per_codeword] Gradient is backpropagated through `x`\n",
    "            tx_loss = tf.square(tf.math.real(p)) + tf.square(tf.math.imag(p)) # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = -bce*tx_loss/rl_perturbation_var # [batch size, num_symbols_per_codeword]\n",
    "            tx_loss = tf.reduce_mean(tx_loss)\n",
    "            return tx_loss, rx_loss\n",
    "        else:\n",
    "            llr = tf.reshape(llr, [-1, n]) # Reshape as expected by the outer decoder\n",
    "            b_hat = self._decoder(llr)\n",
    "            return b,b_hat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65a5114-3cf3-4911-9e15-2863f350ba7c",
   "metadata": {},
   "source": [
    "The next cell implements the training algorithm from [3], which alternates between conventional training of the neural network-based receiver, and RL-based training of the transmitter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8368cd0f-6ecf-4ded-a0a8-fc595a518086",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:54:03.000526Z",
     "iopub.status.busy": "2025-03-09T00:54:03.000276Z",
     "iopub.status.idle": "2025-03-09T00:54:03.007979Z",
     "shell.execute_reply": "2025-03-09T00:54:03.007341Z"
    }
   },
   "outputs": [],
   "source": [
    "def rl_based_training(model):\n",
    "    # Optimizers used to apply gradients\n",
    "    optimizer_tx = tf.keras.optimizers.Adam() # For training the transmitter\n",
    "    optimizer_rx = tf.keras.optimizers.Adam() # For training the receiver\n",
    "\n",
    "    # Function that implements one transmitter training iteration using RL.\n",
    "    @tf.function(jit_compile=True)\n",
    "    def train_tx():\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = tf.random.uniform(shape=[training_batch_size], minval=ebno_db_min, maxval=ebno_db_max)\n",
    "        # Forward pass\n",
    "        with tf.GradientTape() as tape:\n",
    "            # Keep only the TX loss\n",
    "            tx_loss, _ = model(training_batch_size, ebno_db,\n",
    "                               tf.constant(rl_perturbation_var, tf.float32)) # Perturbation are added to enable RL exploration\n",
    "        ## Computing and applying gradients\n",
    "        weights = tape.watched_variables()\n",
    "        grads = tape.gradient(tx_loss, weights, unconnected_gradients=tf.UnconnectedGradients.ZERO)\n",
    "        optimizer_tx.apply_gradients(zip(grads, weights))\n",
    "\n",
    "    # Function that implements one receiver training iteration\n",
    "    @tf.function(jit_compile=True)\n",
    "    def train_rx():\n",
    "        # Sampling a batch of SNRs\n",
    "        ebno_db = tf.random.uniform(shape=[training_batch_size], minval=ebno_db_min, maxval=ebno_db_max)\n",
    "        # Forward pass\n",
    "        with tf.GradientTape() as tape:\n",
    "            # Keep only the RX loss\n",
    "            _, rx_loss = model(training_batch_size, ebno_db) # No perturbation is added\n",
    "        ## Computing and applying gradients\n",
    "        weights = tape.watched_variables()\n",
    "        grads = tape.gradient(rx_loss, weights, unconnected_gradients=tf.UnconnectedGradients.ZERO)\n",
    "        optimizer_rx.apply_gradients(zip(grads, weights))\n",
    "        # The RX loss is returned to print the progress\n",
    "        return rx_loss\n",
    "\n",
    "    # Training loop.\n",
    "    for i in range(num_training_iterations_rl_alt):\n",
    "        # 10 steps of receiver training are performed to keep it ahead of the transmitter\n",
    "        # as it is used for computing the losses when training the transmitter\n",
    "        for _ in range(10):\n",
    "            rx_loss = train_rx()\n",
    "        # One step of transmitter training\n",
    "        train_tx()\n",
    "        # Printing periodically the progress\n",
    "        if i % 100 == 0:\n",
    "            print('Iteration {}/{}  BCE {:.4f}'.format(i, num_training_iterations_rl_alt, rx_loss.numpy()), end='\\r')\n",
    "    print() # Line break\n",
    "\n",
    "    # Once alternating training is done, the receiver is fine-tuned.\n",
    "    print('Receiver fine-tuning... ')\n",
    "    for i in range(num_training_iterations_rl_finetuning):\n",
    "        rx_loss = train_rx()\n",
    "        if i % 100 == 0:\n",
    "            print('Iteration {}/{}  BCE {:.4f}'.format(i, num_training_iterations_rl_finetuning, rx_loss.numpy()), end='\\r')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdd091ad-2ec7-4375-8715-a9516b5a4a4d",
   "metadata": {},
   "source": [
    "In the next cell, an instance of the model defined previously is instantiated and trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f05bcfed-6836-4ee7-9619-9b8ea01245e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:54:03.010583Z",
     "iopub.status.busy": "2025-03-09T00:54:03.010310Z",
     "iopub.status.idle": "2025-03-09T00:55:05.042948Z",
     "shell.execute_reply": "2025-03-09T00:55:05.042143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6900/7000  BCE 0.2767\r\n",
      "Receiver fine-tuning... \n",
      "Iteration 2900/3000  BCE 0.2826\r"
     ]
    }
   ],
   "source": [
    "# Instantiate and train the end-to-end system\n",
    "model = E2ESystemRLTraining(training=True)\n",
    "rl_based_training(model)\n",
    "# Save weights\n",
    "save_weights(model, model_weights_path_rl_training)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cd0345da-9e49-4917-8197-0787254b5cf7",
   "metadata": {},
   "source": [
    "## Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4344594-739d-485f-8ee0-fbb9c8cb9055",
   "metadata": {},
   "source": [
    "The following cell implements a baseline which uses QAM with Gray labeling and conventional demapping for AWGN channel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f81f69a9-cc45-42ad-92a3-8ea469b1363e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:55:05.047007Z",
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     "shell.execute_reply": "2025-03-09T00:55:05.052748Z"
    }
   },
   "outputs": [],
   "source": [
    "class Baseline(Model):\n",
    "\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        self._binary_source = BinarySource()\n",
    "        self._encoder = LDPC5GEncoder(k, n, num_bits_per_symbol)\n",
    "        constellation = Constellation(\"qam\", num_bits_per_symbol, trainable=False)\n",
    "        self.constellation = constellation\n",
    "        self._mapper = Mapper(constellation=constellation)\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        self._channel = AWGN()\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        self._demapper = Demapper(\"app\", constellation=constellation)\n",
    "        self._decoder = LDPC5GDecoder(self._encoder, hard_out=True)\n",
    "\n",
    "    def call(self, batch_size, ebno_db):\n",
    "\n",
    "        # If `ebno_db` is a scalar, a tensor with shape [batch size] is created as it is what is expected by some layers\n",
    "        if len(ebno_db.shape) == 0:\n",
    "            ebno_db = tf.fill([batch_size], ebno_db)\n",
    "        no = ebnodb2no(ebno_db, num_bits_per_symbol, coderate)\n",
    "        no = expand_to_rank(no, 2)\n",
    "\n",
    "        ################\n",
    "        ## Transmitter\n",
    "        ################\n",
    "        b = self._binary_source([batch_size, k])\n",
    "        c = self._encoder(b)\n",
    "        # Modulation\n",
    "        x = self._mapper(c) # x [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Channel\n",
    "        ################\n",
    "        y = self._channel(x, no) # [batch size, num_symbols_per_codeword]\n",
    "\n",
    "        ################\n",
    "        ## Receiver\n",
    "        ################\n",
    "        llr = self._demapper(y, no)\n",
    "        # Outer decoding\n",
    "        b_hat = self._decoder(llr)\n",
    "        return b,b_hat # Ground truth and reconstructed information bits returned for BER/BLER computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9bf294cb-a410-46cf-9844-0e80ed8e1b79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:55:05.055820Z",
     "iopub.status.busy": "2025-03-09T00:55:05.055528Z",
     "iopub.status.idle": "2025-03-09T00:55:05.059167Z",
     "shell.execute_reply": "2025-03-09T00:55:05.058294Z"
    }
   },
   "outputs": [],
   "source": [
    "# Range of SNRs over which the systems are evaluated\n",
    "ebno_dbs = np.arange(ebno_db_min, # Min SNR for evaluation\n",
    "                     ebno_db_max, # Max SNR for evaluation\n",
    "                     0.5) # Step"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6e921887-3d86-4ef1-b550-69bf086a4972",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:55:05.061561Z",
     "iopub.status.busy": "2025-03-09T00:55:05.061260Z",
     "iopub.status.idle": "2025-03-09T00:55:05.066496Z",
     "shell.execute_reply": "2025-03-09T00:55:05.065667Z"
    }
   },
   "outputs": [],
   "source": [
    "# Utility function to load and set weights of a model\n",
    "def load_weights(model, model_weights_path):\n",
    "    model(tf.cast(1, tf.int32), tf.constant(10.0, tf.float32))\n",
    "    with open(model_weights_path, 'rb') as f:\n",
    "        weights = pickle.load(f)\n",
    "    model.set_weights(weights)\n",
    "    points = tf.complex(model.points_r, model.points_i)\n",
    "    model.constellation.points = points"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125b4bd1-911f-4f19-b989-575c765e80d1",
   "metadata": {},
   "source": [
    "The next cell evaluate the baseline and the two autoencoder-based communication systems, trained with different method.\n",
    "The results are stored in the dictionary ``BLER``."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff0fe56c-6e7b-41f7-9b6d-a7919699ec7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:55:05.069408Z",
     "iopub.status.busy": "2025-03-09T00:55:05.069053Z",
     "iopub.status.idle": "2025-03-09T00:58:00.068775Z",
     "shell.execute_reply": "2025-03-09T00:58:00.067945Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.2224e-01 | 1.0000e+00 |       93879 |      768000 |         1024 |        1024 |         5.3 |reached target block errors\n",
      "      4.5 | 9.6033e-02 | 9.9316e-01 |       73753 |      768000 |         1017 |        1024 |         0.1 |reached target block errors\n",
      "      5.0 | 5.8314e-02 | 9.2361e-01 |       50383 |      864000 |         1064 |        1152 |         0.1 |reached target block errors\n",
      "      5.5 | 1.9398e-02 | 5.1807e-01 |       29795 |     1536000 |         1061 |        2048 |         0.2 |reached target block errors\n",
      "      6.0 | 2.4054e-03 | 1.0841e-01 |       16857 |     7008000 |         1013 |        9344 |         1.1 |reached target block errors\n",
      "      6.5 | 1.2466e-04 | 9.1912e-03 |       10172 |    81600000 |         1000 |      108800 |        12.8 |reached target block errors\n",
      "      7.0 | 5.7812e-06 | 5.8594e-04 |         555 |    96000000 |           75 |      128000 |        15.0 |reached max iterations\n",
      "      7.5 | 2.5000e-07 | 4.6875e-05 |          24 |    96000000 |            6 |      128000 |        15.1 |reached max iterations\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0590e-01 | 9.9902e-01 |       81333 |      768000 |         1023 |        1024 |         5.3 |reached target block errors\n",
      "      4.5 | 6.2444e-02 | 8.9931e-01 |       53952 |      864000 |         1036 |        1152 |         0.1 |reached target block errors\n",
      "      5.0 | 2.3795e-02 | 5.7310e-01 |       31981 |     1344000 |         1027 |        1792 |         0.2 |reached target block errors\n",
      "      5.5 | 3.7868e-03 | 1.4612e-01 |       19631 |     5184000 |         1010 |        6912 |         0.8 |reached target block errors\n",
      "      6.0 | 2.5252e-04 | 1.5719e-02 |       12048 |    47712000 |         1000 |       63616 |         7.4 |reached target block errors\n",
      "      6.5 | 1.1938e-05 | 1.0547e-03 |        1146 |    96000000 |          135 |      128000 |        15.0 |reached max iterations\n",
      "      7.0 | 2.6875e-06 | 1.7969e-04 |         258 |    96000000 |           23 |      128000 |        15.1 |reached max iterations\n",
      "      7.5 | 0.0000e+00 | 0.0000e+00 |           0 |    96000000 |            0 |      128000 |        14.9 |reached max iterations\n",
      "\n",
      "Simulation stopped as no error occurred @ EbNo = 7.5 dB.\n",
      "\n",
      "EbNo [dB] |        BER |       BLER |  bit errors |    num bits | block errors |  num blocks | runtime [s] |    status\n",
      "---------------------------------------------------------------------------------------------------------------------------------------\n",
      "      4.0 | 1.0395e-01 | 9.9316e-01 |       79833 |      768000 |         1017 |        1024 |         5.8 |reached target block errors\n",
      "      4.5 | 6.2806e-02 | 9.0972e-01 |       54264 |      864000 |         1048 |        1152 |         0.1 |reached target block errors\n",
      "      5.0 | 2.2637e-02 | 5.3542e-01 |       32597 |     1440000 |         1028 |        1920 |         0.2 |reached target block errors\n",
      "      5.5 | 3.5886e-03 | 1.3925e-01 |       19637 |     5472000 |         1016 |        7296 |         0.9 |reached target block errors\n",
      "      6.0 | 2.4070e-04 | 1.5289e-02 |       11831 |    49152000 |         1002 |       65536 |         7.7 |reached target block errors\n",
      "      6.5 | 1.0688e-05 | 9.8438e-04 |        1026 |    96000000 |          126 |      128000 |        15.1 |reached max iterations\n",
      "      7.0 | 1.3333e-06 | 1.1719e-04 |         128 |    96000000 |           15 |      128000 |        15.1 |reached max iterations\n",
      "      7.5 | 7.2917e-08 | 1.5625e-05 |           7 |    96000000 |            2 |      128000 |        15.1 |reached max iterations\n"
     ]
    }
   ],
   "source": [
    "# Dictionary storing the results\n",
    "BLER = {}\n",
    "\n",
    "model_baseline = Baseline()\n",
    "_,bler = sim_ber(model_baseline, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, graph_mode=\"xla\")\n",
    "BLER['baseline'] = bler.numpy()\n",
    "\n",
    "model_conventional = E2ESystemConventionalTraining(training=False)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "_,bler = sim_ber(model_conventional, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, graph_mode=\"xla\")\n",
    "BLER['autoencoder-conv'] = bler.numpy()\n",
    "\n",
    "model_rl = E2ESystemRLTraining(training=False)\n",
    "load_weights(model_rl, model_weights_path_rl_training)\n",
    "_,bler = sim_ber(model_rl, ebno_dbs, batch_size=128, num_target_block_errors=1000, max_mc_iter=1000, graph_mode=\"xla\")\n",
    "BLER['autoencoder-rl'] = bler.numpy()\n",
    "\n",
    "with open(results_filename, 'wb') as f:\n",
    "    pickle.dump((ebno_dbs, BLER), f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "905f3b1a-f906-4eab-b081-2705c9e1f6ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:58:00.072334Z",
     "iopub.status.busy": "2025-03-09T00:58:00.072131Z",
     "iopub.status.idle": "2025-03-09T00:58:00.531764Z",
     "shell.execute_reply": "2025-03-09T00:58:00.531018Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "# Baseline - Perfect CSI\n",
    "plt.semilogy(ebno_dbs, BLER['baseline'], 'o-', c=f'C0', label=f'Baseline')\n",
    "# Autoencoder - conventional training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-conv'], 'x-.', c=f'C1', label=f'Autoencoder - conventional training')\n",
    "# Autoencoder - RL-based training\n",
    "plt.semilogy(ebno_dbs, BLER['autoencoder-rl'], 'o-.', c=f'C2', label=f'Autoencoder - RL-based training')\n",
    "\n",
    "plt.xlabel(r\"$E_b/N_0$ (dB)\")\n",
    "plt.ylabel(\"BLER\")\n",
    "plt.grid(which=\"both\")\n",
    "plt.ylim((1e-4, 1.0))\n",
    "plt.legend()\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35d47f5-226f-435d-bf65-d46cd0fad5ca",
   "metadata": {},
   "source": [
    "## Visualizing the Learned Constellations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "99fab7bb-d5fd-4173-9acc-688fb395c3a5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:58:00.536107Z",
     "iopub.status.busy": "2025-03-09T00:58:00.535863Z",
     "iopub.status.idle": "2025-03-09T00:58:01.176355Z",
     "shell.execute_reply": "2025-03-09T00:58:01.175517Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_conventional = E2ESystemConventionalTraining(training=True)\n",
    "load_weights(model_conventional, model_weights_path_conventional_training)\n",
    "fig = model_conventional.constellation.show()\n",
    "fig.suptitle('Conventional training');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "772edc3d-3eb5-4219-a8d2-2853f463f84e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:58:01.180158Z",
     "iopub.status.busy": "2025-03-09T00:58:01.179839Z",
     "iopub.status.idle": "2025-03-09T00:58:02.553415Z",
     "shell.execute_reply": "2025-03-09T00:58:02.552774Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_rl = E2ESystemRLTraining(training=False)\n",
    "load_weights(model_rl, model_weights_path_rl_training)\n",
    "fig = model_rl.constellation.show()\n",
    "fig.suptitle('RL-based training');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "c89ebeae-ff4a-44e8-b227-7fd9f56d55a3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2025-03-09T00:58:02.557615Z",
     "iopub.status.busy": "2025-03-09T00:58:02.557307Z",
     "iopub.status.idle": "2025-03-09T00:58:03.147869Z",
     "shell.execute_reply": "2025-03-09T00:58:03.145802Z"
    }
   },
   "outputs": [],
   "source": [
    "%rm awgn_autoencoder_weights_conventional_training awgn_autoencoder_weights_rl_training awgn_autoencoder_results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2774433f-05e1-4421-9e9f-a24dd2f3fd64",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e67ab4d0-2aa8-453d-a810-3c5f105567dd",
   "metadata": {},
   "source": [
    "[1] T. O’Shea and J. Hoydis, \"An Introduction to Deep Learning for the Physical Layer,\" in IEEE Transactions on Cognitive Communications and Networking, vol. 3, no. 4, pp. 563-575, Dec. 2017, doi: 10.1109/TCCN.2017.2758370.\n",
    "\n",
    "[2] S. Cammerer, F. Ait Aoudia, S. Dörner, M. Stark, J. Hoydis and S. ten Brink, \"Trainable Communication Systems: Concepts and Prototype,\" in IEEE Transactions on Communications, vol. 68, no. 9, pp. 5489-5503, Sept. 2020, doi: 10.1109/TCOMM.2020.3002915.\n",
    "\n",
    "[3] F. Ait Aoudia and J. Hoydis, \"Model-Free Training of End-to-End Communication Systems,\" in IEEE Journal on Selected Areas in Communications, vol. 37, no. 11, pp. 2503-2516, Nov. 2019, doi: 10.1109/JSAC.2019.2933891."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.16"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
